Chapitre VI : Conclusion et Perspectives
VI. 2. Perspectives :
VI. 2. Perspectives
:Dans cette thèse, nous avons pu dégager des perspectives pour des futurs travaux de recherche. En effet, nous envisageons en premier lieu d’effectuer une hybridation de l’algorithme de RSMO avec d’autres méta-heuristiques comme la recherche tabou. Ce choix est justifié par le faite que la recherche tabou permet de mémoriser les solutions et donc, de permettre une bonne exploration de l’espace de recherche. Il est à noter également que l’algorithme de recherche tabou est caractérisé par un temps de calcul réduit d’où son adaptation à l’environnement dynamique.
D’un autre point de vue, nous envisageons la modification de notre architecture SMA afin de décomposer la tâche de l’agent véhicule. Nous espérons qu’avec cette décomposition nous une amélioration des résultats. Le principe général de cette décomposition est le suivant : la répartition des critères à optimiser sur des sous-agents véhicule où chaque sous-agent optimise séparément l’objectif accordé. Puis une négociation entre les agents est établie pour générer la meilleure solution. Cette négociation peut être également utilisée entre les agents véhicules eux même pour collaborer afin d’insérer une nouvelle demande, voir l’échange des demandes pour agir comme une méthode de recherche locale.
En ce qui concerne les données utilisés pour valider notre approche, nous avons utilisé deux types de données. Ces données sont le benchmark de (Cordeau et Laporte, 2003) [34] pour le PTD statique et des données aléatoires pour le PTD dynamique. En effet, ces données ne reflètent pas le cas réel, nous envisageons donc d’appliquer notre approche sur des données réelles. Nous espérons avoir des données réelles au pré du service handipole de la compagnie de transport transpole à Lille (France).
122
Les Références Bibliographiques
[1] ZIDI K, Système Interactif d’aide au déplacement Multimodal, thèse de doctorat, Ecole Centrale de Lille, 13 décembre 2006.
[2] http://www.insee.fr/fr/themes/document.asp?ref_id=BDF06
[3] Institut de la Ville en Mouvement – Paris, Séminaire du 21 novembre 2007 http://www.ville-en-mouvement.com/mobilite_des_salaries/indexarticle.html [4] Le covoiturage en France et en Europe – État des lieux et perspectives, Certu, 2007
http://www.certu.fr/fr/Systèmes_de_transports-26/Technologies_des_transportsn84/catalogue/product_info.php?products_id=19 56&language=fr
[5] Cordeau J-F, Laporte G, Savelsbergh MWP, Vigo D (2007) Vehicle Routing, In: C. Barnhart and G. Laporte (eds.), Transportation, Amsterdam: Elsevier
[6] Cordeau J-F (2006) A Branch-and-cut algorithm for the dial-a-ride problem, Operations Research 54:573–586
[7] Savelsbergh MWP (1992) The vehicle routing problem with time windows: Minimizing route duration, ORSA Journal on Computing 4:146–154
[8] Sexton T, Bodin LD (1985) Optimizing single vehicle many-to-many operations with desired delivery times: I. Scheduling, Transportation Science 19:378–410
[9] Dumas Y, Desrosiers J, Soumis F (1989) Large scale multi-vehicle dial-a-ride problems, Les Cahiers du GERAD, G–89–30, HEC Montréal
[11] Psaraftis HN (1980) A dynamic programming approach to the single-vehicle, many-to-many immediate request dial-a-ride problem, Transportation Science 14:130– 154
[12] Psaraftis HN (1983) An exact algorithm for the single-vehicle many-to-many dial-a-ride problem with time windows, Transportation Science 17:351–357
[13] Sexton T (1979) The single vehicle many-to-many routing and scheduling problem, Ph.D. dissertation, SUNY at Stony Brook
[14] Sexton T, Bodin LD (1985a) Optimizing single vehicle many-to-many operations with desired delivery times: I. Scheduling, Transportation Science 19:378–410
[15] Sexton T, Bodin LD (1985b) Optimizing single vehicle many-to-many operations with desired delivery times: II. Routing, Transportation Science 19:411–435
123
[16] Desrosiers J, Dumas Y, Soumis F (1986) A dynamic programming solution of the large-scale single vehicle dial-a-ride problem with time windows, American Journal of Mathematical and Management Sciences 6:301–325
[17] Psaraftis HN (1988) Dynamic vehicle routing problems, In: B.L. Golden, and A.A. Assad (eds.), Vehicle Routing: Method and Studies. Amsterdam: North-Holland, 223– 248
[18] Mitrovi´c-Mini´c S, Krishnamurti R, Laporte G (2004) Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows, Transportation Research B 38:669–685
[19] Jaw J, Odoni AR, Psaraftis HN, Wilson NHM (1986) A heuristic algorithm for the multi-vehicle advance-request dial-a-ride problem with time windows, Transportation Research B 20:243–257
[20] Bodin LD, Sexton T (1986) The multi-vehicle subscriber dial-a-ride problem, TIMS Studies in Management Science 2:73–86
[21] Desrosiers J, Dumas Y, Soumis F, Taillefer S, Villeneuve D(1991) An algorithm for mini-clustering in handicapped transport, Les Cahiers du GERAD, G–91–02, HEC Montreal
[22] Ioachim I, Desrosiers J, Dumas Y, Solomon MM (1995) A request clustering algorithm for door-to-door handicapped transportation, Transportation Science 29:63–78 [23] Toth P, Vigo D (1996) Fast local search algorithms for the handicapped persons
transportation problem In: I.H. Osman, and J.P. Kelly (eds.), Meta-heuristics: Theory and applications. Boston: Kluwer, 677–690
[24] Borndorfer R, Klostermeier F, Gr¨otschel M, K¨uttner C (1997) Telebus Berlin: Vehicle scheduling in a dial-a-ride system, Technical Report SC 97–23, Konrad-Zuse-Zentrum f¨ur Informationstechnik, Berlin
[25] Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models, European Journal of Operational Research 147:451–468
[26] Aldaihani M, Dessouky MM (2003) Hybrid scheduling methods for paratransit operations, Computers & Industrial Engineering 45:75–96
[27] Rekiek B, Delchambre A, Saleh HA (2006) Handicapped person transportation: An application of the grouping genetic algorithm, Engineering Application of Artificial Intelligence 19:511–520
124
[28] Xiang Z, Chu C, Chen H (2006) A fast heuristic for solving a large-scale static dial-a-ride problem under complex constraints, European Journal of Operational Research 174:1117–1139
[29] Wong KI, Bell MGH (2006) Solution of the dial-a-ride problem with multi-dimensional capacity constraints, International Transactions in Operational Research 13:195– 208
[30] Wolfler Calvo R, Colorni A (2006) An effective and fast heuristic for the dial-a-ride problem, 4OR: A Quarterly Journal of Operations Research, forthcoming
[31] Ropke S, Cordeau J-F, Laporte G (2006) Models and branch-and-cut algorithms for pickup and delivery problems with time windows, Networks, forthcoming [32] Melachinoudis E, Ilhan AB, Min H (2007) A dial-a-ride problem for client transportation
in a health-care organization, Computers & Operations Research 34:742–759 [33] Jørgensen RM, Larsen J, Bergvinsdottir KB (2007) Solving the dial-a-ride problem using
genetic algorithms, Journal of the Operational Research Society, forthcoming [34] Cordeau J-F, Laporte G (2003) A tabu search heuristic for the static multi-vehicle
dial-a-ride problem, Transportation Research B 37:579–594
[35] Claudio Cubillos, Nibaldo Rodríguez, Enrique Urra (2009), Application of Genetic Algorithms for the DARPTW Problem. International Journal of Computers, Communications & Control, June 2009
[36] Zidi I. Zidi K. , Ghedira K, . Mesghouni K. (2010), A Multi-Objective Simulated Annealing for the Multi-Criteria Dial a Ride Problem11th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems ,Valenciennes France, 2010.
[37] Madsen OBG, Ravn HF, Rygaard JM (1995) A heuristic algorithm for the a dial-a-ride problem with time windows, multiple capacities, and multiple objectives, Annals of Operations Research 60:193–208
[38] Jaw J, Odoni AR, Psaraftis HN, Wilson NHM (1986) A heuristic algorithm for the multi-vehicle advance-request dial-a-ride problem with time windows, Transportation Research B 20:243–257.
[39] Teodorovic D, Radivojevic G (2000) A fuzzy logic approach to dynamic dial-a-ride problem, Fuzzy Sets and Systems 116:23–33.
[40] Colorni A, Righini G (2001) Modeling and optimizing dynamic dial-a-ride problems, International Transactions in Operational Research 8:155–166
125
[41] Coslovich L, Pesenti R, UkovichW(2006) A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem, European Journal of Operational Research 175:1605–1615
[42] Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models, European Journal of Operational Research 147:451–468
[43] Ulungu E.L and. Teghem J (1994). The two-phases method: an efficient procedure to solve biobjective combinatorial optimization problems. Foundations of Computing and Decision Sciences, 20(2):149-165, 1994.
[44] Ehrgott M. and Gandibleux X (2000). A survey and annotated bibliography of multiobjective combinatorial optimization. OR Spektrum, 22:425-460, 2000. [45] Deb K, Pratap A, and Meyarivan T (2001) Constrained test problems for multi-objective
evolutionary optimization. In Proceedings of Evolutionary Multi-Criterion Optimization, pages 284-298, 2001.
[46] Miettinen K (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, 1999.
[47] Collette Y et Siarry P (2002) Optimisation multiobjectif. Eyrolles, 2002.
[48] Hwang, C. and Masud, A (1979). Multiple objective decision making - methods and applications. In Lectures Notes in Economics and Mathematical Systems, volume 164. Springer-Verlag, Berlin.
[49] Ishibuchi, H. and Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flow shop scheduling. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 28(3) :392–403.
[50] Veldhuizen, D. V., Sandlin, B., Marmelstein, R., Lamont, G., and Terzuoli, A. (1997). Finding improved wire-antenna geometries with genetic algorithms. In Chawdhry, P., Roy, R., and Pant, P., editors, Soft Computing in Engineering Design and Manufacturing, pages 231–240, London. Springer Verlag.
[51] Ritzel, B., Eheart, J., and Ranjithan, S. (1994). Using genetic algorithms to solve a multiple objective groundwater pollution problem. Water Resources Research, 30(5) :1589–1603.
[52] Wienke, P., Lucasius, C., and Kateman, G. (1992). Multicriteria target optimization of analytical procedures using a genetic algorithm. Analytical Chimica Acta, 265(2): 211–225.
126
[53] Coello, C. (1998). Using the min-max method to solve multiobjective optimization problems with genetic algorithms. In IBERAMIA’98, LNCS. Springer-Verlag. [54] Schaffer, J. (1985). Multiple objective optimization with vector evaluated genetic
algorithms. In Grefenstette, J., editor, ICGA Int. Conf. on Genetic Algorithms, pages 93–100. Lawrence Erlbaum.
[55] Richardson, J., Palmer, M., Liepins, G., and Hilliard, M. (1989). Some guidelines for genetic algorithms with penalty functions. In Third Int. Conf. on Genetic Algorithms ICGA’3, pages 191–197.
[56] Surry, P., Radcliffe, N., and Boyd, I. (1995). A multi-objective approach to constraint optimization of gas supply networks: The COMOGA method. In Fogarty, T., editor, Evolutionary Computing, AISB Workshop, LNCS, pages 166–180, Sheffield, U.K. Springer-Verlag.
[57] Jones, B., Crossley, W., and Lyrintzis, A. (1998). Aerodynamic and aeroacoustic optimization of airfoils via a parallel genetic algorithm. In Proc. of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, number AIAI-98-4811, pages 1–11.
[58] Allenson, R. (1992). Genetic algorithms with gender for multi-function optimisation. Technical Report EPCC-SS92-01, Edinburg Parallel Computing Center, Edinburg, Scotland.
[59] Lis, J. and Eiben, A. (1996). A multi-sexual genetic algorithm for multi-objective optimization. In Fukuda, T. and Furuhashi, T., editors, Int. Conf. on Genetic Algorithms ICGA, pages 59–64, Nagoya, Japan.
[60] Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning.Addison-Wesley.
[61] Fonseca, C. and Fleming, P. (1995b). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1–16.
[62] Bentley, P. and Wakefield, J. (1997). Soft Computing in Engineering Design and Manufacturing, chapter Finding acceptable Pareto-optimal solutions using multiobjective genetic algorithms, pages 231–240. Springer Verlag, London. [63] Srinivas, N. and Deb, K. (1995). Multiobjective optimisation using non-dominated
127
[64] Zhou, G. and Gen, M. (1999). Genetic algorithm approach on multi-criteria minimum spanning tree problem. European Journal of Operational Research, 114:141–152 [65] Abdelaziz, F., Krichen, S., and Chaouachi, J. (1999). Meta-heuristics: Advances and
trends in local search paradigms for optimization, chapter A hybrid heuristic for multi-objective knapsack problems, pages 205–212. Kluwer Academic Publishers.
[66] Parks, G. andMiller, I. (1998). Selective breeding in a multiobjective genetic algorithm. In Parallel Problem Solving from Nature PPSN’5, pages 250–259, Amsterdam. Springer-Verkag.
[67] Halhal, D., Walters, G., Ouazar, D., and Savic, D. (1997). Water network rehabilitation with a structured messy genetic algorithm. Journal of Water Resources Planning and Management, 123(3) :137–146.
[68] Baker, J. (1985). Adaptive selection methods for genetic algorithms. In Grefenstette, J., editor, Int. Conf. on Genetic Algorithms and their Applications, pages 101–111, Pittsburg. Lawrence Erlbaum.
[69] Horn, J. and Nafpliotis, N. (1993). Multiobjective optimization using the niched pareto genetic algorithm. Technical Report 93005, University of Illinois, Urbana-Champaign.
[70] Cunha, A., Oliveira, P., and Covas, J. (1997). Use of genetic algorithms in multicriteria optimization to solve industrial problems. In Back, T., editor, Seventh Int. Conf. on Genetic Algorithms ICGA’97, pages 682–688, San Mateo, California. Morgan Kaufmann.
[71] Tamaki, H., Kita, H., and Kobayashi, S. (1996). Multi-objective optimization by genetic algorithms : A review. In IEEE Int. Conf. on Evolutionary Computation ICEC’96, pages 517–522.
[72] Sen, T., Raiszadeh, M., and Dileepan, P. (1988). A branch and bound approach to the bicriterion scheduling problem involving total flowtime and range of lateness. Management Science, 34(2) :254–260.
[73] Stewart, B. and White, C. (1991). Multiobjective A*. Journal of the ACM, 38(4):775– 814.
[74] White, D. (1982). The set of efficient solutions for multiple-objectives shortest path problems. Computers and Operations Research, 9 :101–107.
128
[75] Gandibleux, X., Mezdaoui, N., and Freville, A. (1996). A tabu search procedure to solve multiobjective combinatorial optimization problems. In Caballero, R., Ruiz, F., and Steuer, R., editors, Second Int. Conf. on Multi-Objective Programming and Goal Programming MOPGP’96, pages 291–300, Torremolinos, Spain. Springer-Verlag.
[76] Ulungu, E. (1993). Optimisation combinatoire multicritère : détermination de l’ensemble des solutions efficaces et méthodes interactives. PhD thesis, Université de Mons-Hainaut.
[77] Gandibleux, X., Libert, G., Cartignies, E., and Millot, P. (1994). SMART : étude de la faisabilité d’un solveur de problèmes de mobilisation de réserve tertiaire. Revue des systèmes de Décision, 3(1) :45–67.
[78] Srinivas, N. and Deb, K. (1995). Multiobjective optimisation using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2(8) :221–248.
[79] Fonseca, C. and Fleming, P. (1995a). Multiobjective genetic algorithms made easy: Selection, sharing and mating restrictions. In IEEE Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, pages 45–52, Sheffield, UK.
[80] Wienke, P., Lucasius, C., and Kateman, G. (1992). Multicriteria target optimization of analytical procedures using a genetic algorithm. Analytical Chimica Acta, 265(2) :211–225.
[81] Kirkpatrick S, Gelatt C.D, and Vecchi M.P. Optimization by simulated annealing. Science, 220:671{680, 1983.
[82] Aarts E.H.L and Korst J. Simulated annealing and boltzmann machines: a stochastic approach to combinatorial and neural computing. Wiley, Chichester, 1989.
[83] Vidal R.V. (1993). Applied simulated annealing. Lecture Notes in Economics and Mathematical
Systems, 396, 1993. Springer-Verlag.
[84] Koulamas C., Anthony S.R., and Jean R. (1994). A survey of simulated annealing application to operations research problems. OMEGA, 22:41-56, 1994.
[85] Glover F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13(5):533-549, 1986.
129
[87] Back T., Fogel D.B., Michalewicz Z., and Baeck T (1997). Handbook of Evolutionary Computation. Institute of Physics Publishing and Oxford University Press, 1997. [88] Goldberg D.E. (1989) Genetic algorithms for search, optimization, and machine learning.
Reading, MA: Addison-Wesley, 1989
[89] Schwefel H-P. (1981) Numerical optimization of computer models. Wiley, Chichester, 1981.
[90] Fogel D. (2000) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (second edition). IEEE Press, 2000.
[91] Inge Li Gørtz, (2006). Hardness of Preemptive Finite Capacity Dial-a-Ride. Institut for Matematik og Datalogi Syddansk Universitet, http://www.imada.sdu.dk Preprints 2006 No. 4 February 2006 ISSN No. 0903-3920.
[92] Boudali I. ,Fki W., Ghédira K., (2004). How to deal with the VRPTW by using multi-agent coalitions. Fourth International Conference on Hybrid Intelligent Systems (HIS'04), Kitakyushu, Japan,2004
[93] Cordeau, J.F. and Laporte G., (2006). The Dial-a-Ride Problem: Models and Algorithms. Les Cahiers du GERAD G–2006–78 Copyright c 2006 GERAD
[94] Suman B.and Kumar P. (2006), .A survey of simulated annealing as a tool for single and multiobjective optimization,. Journal of the Operational Research Society, vol. 57, no. 10, pp. 1143.1160, 2006.
[95] Mauri G.R, Lorena L.A.N, (2006). A Multiobjective Model and Simulated Annealing Approach for a Dial-a-Ride Problem. Workshop dos Cursos de Computação 2006.
[96] Ferber J. (1995) Les Systèmes Multi-agents : Vers un intelligence collective. Informatique et Intelligence Artificielle, InterEditions, Paris, 1995.
[97] Labrie M.A (2004), Langage de communication agent basé sur les engagements par l’entremise des jeux de dialogue, Mémoire présenté `a la Faculté des études supérieures de l’Université Laval pour l’obtention du grade de maître des sciences, Janvier 2004
[98] Florez-Mendez R.A (1999). Towards a standardization of Multi-Agent System Frameworks. ACM Crossroads Student Magazine, Canada, 1999
[99] Sycara K.P. (1998). Multi-Agent Systems. American Association for Artificial Intelligence, AI Magazine, pages 79-92, 1998.
130
[100] Vauvert G. and Seghrouchni A.E (2000). Formation de coalitions pour agents rationnels. In : Proceedings des JLIPN’2000, pages 11-12, Villetaneuse, France, Septembre, 2000.
[101] Zidi I. , Zidi K. , Mesghouni K., Ghedira K. (2011) ,A Multi-Agent System based on the Multi-Objective Simulated Annealing Algorithm for the Static Dial a Ride Problem, 18th World Congress of the International Federation of Automatic Control (IFAC).
[102] Bergvinsdottir K.B., (2004) . The genetic algorithm for solving the dial-a-ride problem. Master Thesis of Science in Engineering. Department of Informatics and Mathematical Modelling (IMM), Technical University of Denmark (DTU). [103] Durfee E. H. and Lesser V. R. (1991), Partial global planning: A coordination
framework for distributed hypothesis formation, IEEE Transactions on Systems, Man, and Cybernetics, 21(5), pp.1167-1183, September-October 1991.
[104] Chaib-draa B. (1996) Interaction between agents in routine, familiar and unfamiliar situations. International Journal of Intelligent and Cooperative Information Systems, 1(5):7-20, 1996.
[105] Ljungberg M. and Lucas A. (1992), “The oasis air-traffic management system”, In Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence, PRICAI '92, Seoul, Korea, 1992.
[106] Cammarata S. McArthur D. and Steeb R. (1983) Strategies of cooperation in distributed problem solving. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83), Karlsruhe, Germany, 1983.
[107] Fischer K., Müller J.P, Pischel M.and Schier D. (1995) A Model for Cooperative Transportation Scheduling, In Proceedings of the First International Conference on MAS, AAAI Press/MIT Press, pp.109-116, Menlo Park, California,1995. [108] Chaib-draa B. (1994) Distributed Artificial Intelligence: An overview. In A. Ken, J. G.
Williams, C. M. Hall, and R.Kent, editors, Encyclopedia Of Computer Science And Technology, volume 31, pages 215-243. Marcel Dekker, Inc, 1994.
[109] Gruer P., Hilaire V. and Koukam A. (2001) Multi-Agent Approach to Modelling and Simulation of Urban Transportation Systems, Proceedings of the 2001 IEEE SMC Conference, 6-10 October 2001, Tucson, Arizona, USA, pp.2499-2504.
131
[110] Said El Hmam M., Jolly D., Abouaissa H., Benasser A. (2005) Modélisation Hybride du Flux de Trafic. Workshop avec école intégrée Méthodologies et Heuristiques pour l’Optimisation des Systèmes Industriels 24-26 Avril 2005, Hammamet, Tunisie. Pages193-198.
[111] Saussol B., Maouche S., Hayat S., Dekokere A. Dumont A (2000), Elaboration et mise au point d’un système d’aide à la décision pour la gestion du réseau de transport collectif de Montbéliar, Rapport d’étape INRETS-I3D-LAIL, Appui à la modélisation du système multi-agents, juin 2000.
[112] Laïchour H., Maouche S.and Mandiau R. (2001) Traffic Control Assistance in Connection nodes, Proceedings of the 2001 IEEE SMC Conference, 6-10 October 2001, Tucson, Arizona, USA.
[113] Zidi I. , Zidi K. , Mesghouni K., Ghedira K. (2010) , Application de l'Algorithme de Recuit Simulé pour la Résolution d'un Problème de Transport à la Demande Bi-objectif, La Cinquième Conférence Internationale en Recherche Opérationnelle, Marrakech Maroc,2010.
[114] Talbi E-G., Rahoual M.,. Mabed M.H, and Dhaenens C. (2001) New genetic approach for multicriteria optimization problems: Application to the flow shop. In Evolutionary Multi-criterion Optimization (EMO), volume LNCS 1993, pages 416–428. Zurich, Switzerland, 2001.
[115] Issam Zidi, Kamel Zidi, Khaled Ghedira, Khaled Mesghouni, 11th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems ,Valenciennes France,2010. IEEE.
[116] Issam Zidi, Kamel Zidi, Khaled Ghedira, Khaled Mesghouni, special issue on engineering management of the International journal on engineering management and economics
132
Modélisation et Optimisation du Problème de Transport à la Demande Multicritère et Dynamique
Résumé : Le Problème de Transport à la Demande (PTD), consiste à prendre en charge le
transport des personnes à partir d'un lieu de départ vers un lieu d'arrivé. Il est caractérisé par un ensemble de demandes de transport et d'un nombre de véhicules disponible. L'ultime objectif dans ce travail de thèse est d'offrir une alternative optimisée au déplacement individuel et collectif.
Le PTD est classé parmi les problèmes NP-difficile, la majorité des travaux de recherche ont été concentrés sur l'utilisation des méthodes approchées pour le résoudre.
En plus, il s'avère également multicritère, la solution proposée dans ce travail permet à la fois une réduction du temps de voyage concernant les demandes de transport ainsi que la réduction de la distance parcourue. Dans ce rapport de thèse, nous proposons notre contribution à l'étude et à la résolution du problème de transport à la demande multicritère et dynamique en appliquant l'algorithme de recuit simulé multi-objectif. Une grande partie de notre travail concerne la conception, le développement et la validation des approches qui permettent de donner des solutions optimales ou quasi optimales, pour un PTD. Ces approches utilisent une méthode multicritère qui s’appuie sur l’algorithme de recuit simulé. La modélisation du PTD est représentée par une architecture multi-acteurs. Cette architecture nous montre l’aspect distribué du système, les interactions et les relations qui peuvent avoir lieu entre les différents acteurs. Nous présentons dans ce travail un Système Multi-Agents pour la planification des itinéraires des véhicules affectées au transport des voyageurs. Les agents de ce système utilisent le module d’optimisation développé dans la première partie.
Mots-clefs : Heuristiques, transport à la demande, optimisation multi-objectif, algorithme de
recuit simulé multi-objectif, système multi-agents.
Modeling and Optimization a Dynamic and multicriteria Dial a Ride Problem.
Abstract: The Dial a Ride Problem (DRP) is to take passengers from a place of departures to
places of arrivals. Different versions of the dynamic Dial a Ride Problem are found in every day practice; transportation of people in low-density areas, transportation of the handicapped and elderly persons and parcel pick-up and delivery service in urban areas. In the DRP, customers send transportation requests to an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. The DRP is classified as NP-hard problem that’s why most research has been concentrated on the use of approximate methods to solve it. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this thesis, we offer our contribution to the study and solving the DRP in the application using a multi agent system based on the Multi-Objective Simulated Annealing Algorithm.
Keywords: Heuristics, Dial a Ride Problem, Passenger Transportation, Multi-Criteria